{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "72cb4fbdcf8a9b882a5dfaa1449a3520ae529639"
   },
   "source": [
    "# House Prices: Advanced Regression Techniques\n",
    "### Predict sales prices with detailed feature engineering, automatic outlier detection, Advanced Regression Techniques(GradientBoosting,Xgboost...) and Stacking\n",
    "![main](http://rasbt.github.io/mlxtend/user_guide/classifier/StackingCVClassifier_files/stacking_cv_algorithm.png)\n",
    "\n",
    "<br>\n",
    "\n",
    "**Competition Description**\n",
    "\n",
    "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n",
    "\n",
    "With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n",
    "\n",
    "**Summary**\n",
    "- 1.Exploratory Data Analysis (EDA):distribution,outliers...\n",
    "- 2.Personalized Feature Engineering\n",
    "- 3.Advanced Regression Techniques\n",
    "- 4.Ensemble Learning\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "### Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd \n",
    "from datetime import datetime\n",
    "from scipy.stats import skew\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV, Ridge \n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from mlxtend.regressor import StackingCVRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Exploratory Data Analysis (EDA)\n",
    "### 了解数据的分布：特征工程的基础\n",
    "* 建议多用describe函数观察特征、target的分布情况\n",
    "* 画出Correlation matrix、散点图、直方图等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: (1460, 81)\n",
      "Test set size: (1459, 80)\n",
      "start data processing 2024-04-05 17:13:45.171023\n"
     ]
    }
   ],
   "source": [
    "# print(os.listdir(\"../input\"))\n",
    "\n",
    "train = pd.read_csv('./data/train.csv')\n",
    "test = pd.read_csv('./data/test.csv')\n",
    "\n",
    "train\n",
    "\n",
    "print(\"Train set size:\", train.shape)\n",
    "print(\"Test set size:\", test.shape)\n",
    "print('start data processing', datetime.now(), )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 know your target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# know your target\n",
    "train['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\Users\\frank\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  ndim = x[:, None].ndim\n",
      "c:\\Users\\frank\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\axes\\_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "c:\\Users\\frank\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\matplotlib\\axes\\_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['SalePrice']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.882876\n",
      "Kurtosis: 6.536282\n"
     ]
    }
   ],
   "source": [
    "#skewness and kurtosis: 可以看到SalePrice的偏度较大，log变换可以缓解这个问题，而且比赛的损失函数也正好是log-rmse，所以随后会对SalePrice作log-transformation\n",
    "print(\"Skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"Kurtosis: %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.121347\n",
      "Kurtosis: 0.809519\n"
     ]
    }
   ],
   "source": [
    "# We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "#much better\n",
    "print(\"Skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"Kurtosis: %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Relevance of features-target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.247699</td>\n",
       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>856</td>\n",
       "      <td>856</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.109016</td>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.317171</td>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>920</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.849405</td>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>756</td>\n",
       "      <td>961</td>\n",
       "      <td>1</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.429220</td>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>12.072547</td>\n",
       "      <td>6</td>\n",
       "      <td>1647</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>953</td>\n",
       "      <td>953</td>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>12.254868</td>\n",
       "      <td>6</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>1542</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>1978</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>12.493133</td>\n",
       "      <td>7</td>\n",
       "      <td>2340</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>1152</td>\n",
       "      <td>1188</td>\n",
       "      <td>2</td>\n",
       "      <td>1941</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>11.864469</td>\n",
       "      <td>5</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>1078</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>11.901590</td>\n",
       "      <td>5</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "      <td>1256</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  OverallQual  GrLivArea  GarageCars  GarageArea  TotalBsmtSF  \\\n",
       "0     12.247699            7       1710           2         548          856   \n",
       "1     12.109016            6       1262           2         460         1262   \n",
       "2     12.317171            7       1786           2         608          920   \n",
       "3     11.849405            7       1717           3         642          756   \n",
       "4     12.429220            8       2198           3         836         1145   \n",
       "...         ...          ...        ...         ...         ...          ...   \n",
       "1455  12.072547            6       1647           2         460          953   \n",
       "1456  12.254868            6       2073           2         500         1542   \n",
       "1457  12.493133            7       2340           1         252         1152   \n",
       "1458  11.864469            5       1078           1         240         1078   \n",
       "1459  11.901590            5       1256           1         276         1256   \n",
       "\n",
       "      1stFlrSF  FullBath  YearBuilt  YearRemodAdd  \n",
       "0          856         2       2003          2003  \n",
       "1         1262         2       1976          1976  \n",
       "2          920         2       2001          2002  \n",
       "3          961         1       1915          1970  \n",
       "4         1145         2       2000          2000  \n",
       "...        ...       ...        ...           ...  \n",
       "1455       953         2       1999          2000  \n",
       "1456      2073         2       1978          1988  \n",
       "1457      1188         2       1941          2006  \n",
       "1458      1078         1       1950          1996  \n",
       "1459      1256         1       1965          1965  \n",
       "\n",
       "[1460 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=10\n",
    "re_cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index\n",
    "train[re_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.81718461, 0.70092699, 0.68062487, 0.65088768,\n",
       "        0.61213423, 0.59698132, 0.59477066, 0.58657019, 0.56560778],\n",
       "       [0.81718461, 1.        , 0.59300743, 0.60067072, 0.56202176,\n",
       "        0.5378085 , 0.47622383, 0.55059971, 0.57232277, 0.55068392],\n",
       "       [0.70092699, 0.59300743, 1.        , 0.46724742, 0.46899748,\n",
       "        0.4548682 , 0.56602397, 0.63001165, 0.19900971, 0.28738852],\n",
       "       [0.68062487, 0.60067072, 0.46724742, 1.        , 0.88247541,\n",
       "        0.43458483, 0.43931681, 0.46967204, 0.53785009, 0.42062215],\n",
       "       [0.65088768, 0.56202176, 0.46899748, 0.88247541, 1.        ,\n",
       "        0.48666546, 0.48978165, 0.40565621, 0.47895382, 0.37159981],\n",
       "       [0.61213423, 0.5378085 , 0.4548682 , 0.43458483, 0.48666546,\n",
       "        1.        , 0.81952998, 0.32372241, 0.391452  , 0.29106558],\n",
       "       [0.59698132, 0.47622383, 0.56602397, 0.43931681, 0.48978165,\n",
       "        0.81952998, 1.        , 0.38063749, 0.28198586, 0.24037927],\n",
       "       [0.59477066, 0.55059971, 0.63001165, 0.46967204, 0.40565621,\n",
       "        0.32372241, 0.38063749, 1.        , 0.46827079, 0.43904648],\n",
       "       [0.58657019, 0.57232277, 0.19900971, 0.53785009, 0.47895382,\n",
       "        0.391452  , 0.28198586, 0.46827079, 1.        , 0.59285498],\n",
       "       [0.56560778, 0.55068392, 0.28738852, 0.42062215, 0.37159981,\n",
       "        0.29106558, 0.24037927, 0.43904648, 0.59285498, 1.        ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(train[re_cols].values.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#saleprice correlation matrix\n",
    "k = 10 #number of variables for heatmap\n",
    "cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index\n",
    "cm = np.corrcoef(train[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从图中可以看出：\n",
    "\n",
    "* OverallQual，GrLivArea 以及 TotalBsmtSF  与 SalePrice 有很强的相关性。\n",
    "* GarageCars 和 GarageArea 也是相关性比较强的变量. 车库中存储的车的数量是由车库的面积决定的，它们就像双胞胎，所以不需要专门区分 GarageCars 和 GarageAre，所以我们只需要其中的一个变量。这里我们选择了 GarageCars，因为它与 SalePrice 的相关性更高一些。\n",
    "* TotalBsmtSF  和 1stFloor 与上述情况相同，我们选择 TotalBsmtS 。\n",
    "* FullBath 几乎不需要考虑。\n",
    "* TotRmsAbvGrd 和 GrLivArea 也是变量中的双胞胎。\n",
    "* YearBuilt 和 SalePrice 相关性似乎不强。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\seaborn\\axisgrid.py:1969: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x243ab6099a0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot\n",
    "sns.set()\n",
    "cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']\n",
    "sns.pairplot(train[cols], size = 2.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 automatic outlier detecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_outliers(x, y, top=5, plot=True):\n",
    "    lof = LocalOutlierFactor(n_neighbors=40, contamination=0.1)\n",
    "    x_ =np.array(x).reshape(-1,1)\n",
    "    preds = lof.fit_predict(x_)\n",
    "    lof_scr = lof.negative_outlier_factor_\n",
    "    out_idx = pd.Series(lof_scr).sort_values()[:top].index\n",
    "    if plot:\n",
    "        f, ax = plt.subplots(figsize=(9, 6))\n",
    "        plt.scatter(x=x, y=y, c=np.exp(lof_scr), cmap='RdBu')\n",
    "    return out_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1298, 523, 1182, 691, 533], dtype='int64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outs = detect_outliers(train['GrLivArea'], train['SalePrice'],top=5) #got 1298,523\n",
    "outs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1298   -8.974362\n",
       "523    -5.968408\n",
       "1182   -5.366630\n",
       "691    -4.899521\n",
       "533    -4.811926\n",
       "          ...   \n",
       "136    -0.954031\n",
       "1404   -0.954031\n",
       "483    -0.953431\n",
       "575    -0.953431\n",
       "339    -0.949657\n",
       "Length: 1460, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lof = LocalOutlierFactor(n_neighbors=40, contamination=0.1)\n",
    "x_ = np.array(train['GrLivArea']).reshape(-1, 1)\n",
    "lof.fit_predict(x_)\n",
    "lof_scr = lof.negative_outlier_factor_\n",
    "pd.Series(lof_scr).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([185, 170, 635, 1009, 88], dtype='int64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outs = detect_outliers(train['LowQualFinSF'], train['SalePrice'],top=5)#got 88\n",
    "outs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#很多public kernel中都用这些点，88,523,1298很容易找到，对于其他的outliers后面会补充说明\n",
    "#改进点８：more or less outliers\n",
    "outliers = [30, 88, 462, 523, 632, 1298, 1324]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 4), (533, 4), (1298, 4), (1270, 3), (375, 3), (523, 3), (635, 3), (634, 3), (976, 3), (975, 3), (978, 3), (977, 3), (313, 2), (335, 2), (916, 2), (1213, 2), (812, 2), (77, 2), (7, 2), (953, 2), (496, 2), (1182, 2), (954, 2), (597, 2), (1163, 2), (1350, 2), (1328, 2), (495, 2), (1459, 1), (1, 1), (1458, 1), (1457, 1), (164, 1), (873, 1), (589, 1), (555, 1), (249, 1), (706, 1), (451, 1), (636, 1), (1100, 1), (304, 1), (508, 1), (218, 1), (1442, 1), (1058, 1), (240, 1), (1166, 1), (591, 1), (277, 1), (771, 1), (1140, 1), (1223, 1), (699, 1), (219, 1), (229, 1), (790, 1), (930, 1), (1028, 1), (695, 1), (645, 1), (1149, 1), (125, 1), (599, 1), (574, 1), (332, 1), (440, 1), (1024, 1), (1373, 1), (431, 1), (1400, 1), (185, 1), (170, 1), (1009, 1), (88, 1), (691, 1), (738, 1), (188, 1), (326, 1), (624, 1), (298, 1), (1283, 1), (53, 1), (189, 1), (809, 1), (48, 1), (203, 1), (434, 1), (1218, 1), (642, 1), (166, 1), (309, 1), (605, 1), (1190, 1), (747, 1), (420, 1), (1340, 1), (542, 1), (1372, 1), (516, 1), (351, 1), (1060, 1), (499, 1), (63, 1), (1397, 1), (131, 1), (874, 1), (155, 1), (9, 1), (297, 1), (1059, 1), (341, 1), (1349, 1), (198, 1), (935, 1), (205, 1), (55, 1), (5, 1), (1437, 1), (1346, 1), (1012, 1), (1443, 1), (588, 1), (995, 1), (1423, 1), (810, 1), (1170, 1), (1386, 1), (867, 1), (51, 1), (968, 1), (30, 1)]\n",
      "30\n",
      "88\n",
      "523\n",
      "1298\n"
     ]
    }
   ],
   "source": [
    "#all_outliers只包含30,88,523,1298，其他的outliers是怎么得到的？\n",
    "#可能的原因：\n",
    "#1.detect_outliers函数中的参数设置问题\n",
    "#2.这里仅从特征与train['SalePrice']的关系来寻找outliers,或许也可以从特征与特征之间的关系来寻找outliers\n",
    "from collections import Counter\n",
    "all_outliers=[]\n",
    "numeric_features = train.dtypes[train.dtypes != 'object'].index\n",
    "for feature in numeric_features:\n",
    "    try:\n",
    "        outs = detect_outliers(train[feature], train['SalePrice'],top=5, plot=False)\n",
    "    except:\n",
    "        continue\n",
    "    all_outliers.extend(outs)\n",
    "\n",
    "print(Counter(all_outliers).most_common())\n",
    "for i in outliers:\n",
    "    if i in all_outliers:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1453, 81)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#delete outliers\n",
    "train = train.drop(train.index[outliers])\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 concat train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2912, 79)\n"
     ]
    }
   ],
   "source": [
    "#合并train,test的特征，便于统一进行特征工程\n",
    "y = train.SalePrice.reset_index(drop=True)\n",
    "train_features = train.drop(['SalePrice'], axis=1)\n",
    "test_features = test\n",
    "features = pd.concat([train_features, test_features]).reset_index(drop=True)\n",
    "# Now drop the  'Id' colum since it's unnecessary for  the prediction process.\n",
    "features.drop(['Id'], axis=1, inplace=True)\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.1 一些特征其被表示成数值特征缺乏意义，例如年份还有类别(有些类别使用数字表示，会被误认为是数值变量)，这里将其转换为字符串，即类别型变量\n",
    "features['MSSubClass'] = features['MSSubClass'].apply(str)\n",
    "features['YrSold'] = features['YrSold'].astype(str)\n",
    "features['MoSold'] = features['MoSold'].astype(str)\n",
    "# 改进点1：OverallQual，OverallCond也是由数字表示的类别变量，但内含顺序信息\n",
    "# features['OverallQual'] = features['OverallQual'].astype(str)\n",
    "# features['OverallCond'] = features['OverallCond'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',\n",
       "       'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
       "       'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea',\n",
       "       'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr',\n",
       "       'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt',\n",
       "       'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour',\n",
       "       'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1',\n",
       "       'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl',\n",
       "       'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond',\n",
       "       'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',\n",
       "       'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical',\n",
       "       'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType',\n",
       "       'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.2 numeric_features and \n",
    "numeric_features = features.dtypes[features.dtypes != 'object'].index\n",
    "numeric_features\n",
    "len(numeric_features) #33\n",
    "category_features = features.dtypes[features.dtypes == 'object'].index\n",
    "category_features\n",
    "len(category_features) #46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.3 special features with NA---> NO such feature（NA不是真正的缺失值，而是该样本没有这个特征)\n",
    "special_features = [\n",
    "    'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
    "    'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond',\n",
    "    'PoolQC', 'Fence'\n",
    "]\n",
    "len(special_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 fill missing values: 先特殊再一般\n",
    "### 一般情况：\n",
    "* numeric_features: 一般填充均值，对于其中的special_features，填0\n",
    "* category_features: 一般填充众数，少数可以填充中位数等\n",
    "\n",
    "### 特殊情况：\n",
    "* Functional,Electrical,KitchenQual具有典型值，应填充典型值\n",
    "* MSZoning要按MSSubClass分组填充众数\n",
    "* LotFrontage按Neighborhood分组填充中位数(房子到街道的距离先按照地理位置分组再填充各自的中位数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       RL\n",
       "1       RL\n",
       "2       RL\n",
       "3       RL\n",
       "4       RL\n",
       "        ..\n",
       "2907    RM\n",
       "2908    RM\n",
       "2909    RL\n",
       "2910    RL\n",
       "2911    RL\n",
       "Name: MSZoning, Length: 2912, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode()[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.类别型特征，但明说了具有典型值的：fillna with Typical values\n",
    "features['Functional'] = features['Functional'].fillna('Typ') #Typ\tTypical Functionality\n",
    "features['Electrical'] = features['Electrical'].fillna(\"SBrkr\") #SBrkr\tStandard Circuit Breakers & Romex\n",
    "features['KitchenQual'] = features['KitchenQual'].fillna(\"TA\") #TA\tTypical/Average\n",
    "\n",
    "#2.分组填充\n",
    "#groupby：Group DataFrame or Series using a mapper or by a Series of columns.\n",
    "#transform是与groupby（pandas中最有用的操作之一）组合使用的,恢复维度\n",
    "#对MSZoning按MSSubClass分组填充众数\n",
    "features['MSZoning'] = features.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode()[0]))\n",
    "#对LotFrontage按Neighborhood分组填充中位数(房子到街道的距离先按照地理位置分组再填充各自的中位数)\n",
    "features['LotFrontage'] = features.groupby('Neighborhood')['LotFrontage'].transform(lambda x: x.fillna(x.median()))\n",
    "\n",
    "#3. fillna with new type: ‘None’(或者其他不会和已有类名重复的str）\n",
    "features[\"PoolQC\"] = features[\"PoolQC\"].fillna(\"None\") #note \"None\" is a str, (NA\tNo Pool)\n",
    "#车库相关的类别变量，使用新类别字符串'None'填充空值。\n",
    "for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:\n",
    "    features[col] = features[col].fillna('None')\n",
    "#地下室相关的类别变量，使用字符串'None'填充空值。\n",
    "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
    "    features[col] = features[col].fillna('None')\n",
    "\n",
    "#4. fillna with 0: 数值型的特殊变量\n",
    "#车库相关的数值型变量，使用0填充空值。\n",
    "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
    "    features[col] = features[col].fillna(0)\n",
    "\n",
    "#5.填充众数\n",
    "#对于列名为'Exterior1st'、'Exterior2nd'、'SaleType'的特征列，使用列中的众数填充空值。\n",
    "features['Exterior1st'] = features['Exterior1st'].fillna(features['Exterior1st'].mode()[0]) \n",
    "features['Exterior2nd'] = features['Exterior2nd'].fillna(features['Exterior2nd'].mode()[0])\n",
    "features['SaleType'] = features['SaleType'].fillna(features['SaleType'].mode()[0])\n",
    "\n",
    "#6. 统一填充剩余的数值特征和类别特征\n",
    "features[numeric_features] = features[numeric_features].apply(\n",
    "            lambda x: x.fillna(0)) #改进点2：没做标准化，这里把0换成均值更好吧？\n",
    "features[category_features] = features[category_features].apply(\n",
    "            lambda x: x.fillna('None')) #改进点3：可以考虑将新类别'None'换成众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\frank\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\scipy\\stats\\stats.py:3913: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(PearsonRConstantInputWarning())\n",
      "c:\\Users\\frank\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\scipy\\stats\\stats.py:3943: PearsonRNearConstantInputWarning: An input array is nearly constant; the computed correlation coefficent may be inaccurate.\n",
      "  warnings.warn(PearsonRNearConstantInputWarning())\n"
     ]
    }
   ],
   "source": [
    "#2.5 data transformation\n",
    "#数字型数据列偏度校正\n",
    "#使用skew()方法，计算所有整型和浮点型数据列中，数据分布的偏度（skewness）。\n",
    "#偏度是统计数据分布偏斜方向和程度的度量，是统计数据分布非对称程度的数字特征。亦称偏态、偏态系数。 \n",
    "skew_features = features[numeric_features].apply(lambda x: skew(x)).sort_values(ascending=False)\n",
    "\n",
    "#改进点5：调整阈值，原文以0.5作为基准，统计偏度超过此数值的高偏度分布数据列，获取这些数据列的index\n",
    "high_skew = skew_features[skew_features > 0.15]\n",
    "skew_index = high_skew.index\n",
    "\n",
    "#对高偏度数据进行处理，将其转化为正态分布\n",
    "#Box和Cox提出的变换可以使线性回归模型满足线性性、独立性、方差齐次以及正态性的同时，又不丢失信息\n",
    "#也可以使用简单的log变换\n",
    "for i in skew_index:\n",
    "    features[i] = boxcox1p(features[i], boxcox_normmax(features[i] + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.6 特征删除和融合创建新特征\n",
    "#features['Utilities'].describe()\n",
    "#Utilities: all values are the same(AllPub 2914/2915)\n",
    "#Street: Pave 2905/2917\n",
    "#PoolQC: too many missing values, del_features = ['PoolQC', 'MiscFeature', 'Alley', 'Fence','FireplaceQu'] missing>50%\n",
    "#改进点4：删除更多特征del_features = ['PoolQC', 'MiscFeature', 'Alley', 'Fence','FireplaceQu']\n",
    "features = features.drop(['Utilities', 'Street', 'PoolQC',], axis=1) \n",
    "#features = features.drop(['Utilities', 'Street', 'PoolQC','MiscFeature', 'Alley', 'Fence'], axis=1) #FireplaceQu建议保留\n",
    "\n",
    "#融合多个特征，生成新特征\n",
    "#改进点6：可以尝试组合出更多的特征\n",
    "features['YrBltAndRemod']=features['YearBuilt']+features['YearRemodAdd']\n",
    "features['TotalSF']=features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF']\n",
    "\n",
    "features['Total_sqr_footage'] = (features['BsmtFinSF1'] + features['BsmtFinSF2'] +\n",
    "                                 features['1stFlrSF'] + features['2ndFlrSF'])\n",
    "\n",
    "features['Total_Bathrooms'] = (features['FullBath'] + (0.5 * features['HalfBath']) +\n",
    "                               features['BsmtFullBath'] + (0.5 * features['BsmtHalfBath']))\n",
    "\n",
    "features['Total_porch_sf'] = (features['OpenPorchSF'] + features['3SsnPorch'] +\n",
    "                              features['EnclosedPorch'] + features['ScreenPorch'] +\n",
    "                              features['WoodDeckSF'])\n",
    "\n",
    "#简化特征。对于某些分布单调（比如100个数据中有99个的数值是0.9，另1个是0.1）的数字型数据列，进行01取值处理。\n",
    "#PoolArea: unique      13, top          0, freq      2905/2917\n",
    "#2ndFlrSF: unique      633, top          0, freq      1668/2917\n",
    "#2ndFlrSF: unique      5, top          0, freq      1420/2917\n",
    "features['haspool'] = features['PoolArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['has2ndfloor'] = features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasgarage'] = features['GarageArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasbsmt'] = features['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasfireplace'] = features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before get_dummies: (2912, 86)\n",
      "after get_dummies: (2912, 333)\n",
      "after get_dummies, the dataset size: X (1453, 333) y (1453,) X_sub (1459, 333)\n"
     ]
    }
   ],
   "source": [
    "#2.7 get_dummies\n",
    "print(\"before get_dummies:\",features.shape)\n",
    "final_features = pd.get_dummies(features).reset_index(drop=True)\n",
    "print(\"after get_dummies:\",final_features.shape)\n",
    "\n",
    "X = final_features.iloc[:len(y), :]\t\n",
    "X_sub = final_features.iloc[len(y):, :]\n",
    "print(\"after get_dummies, the dataset size:\",'X', X.shape, 'y', y.shape, 'X_sub', X_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X (1453, 331) y (1453,) X_sub (1459, 331)\n",
      "feature engineering finished! 2024-04-05 17:31:59.955182\n"
     ]
    }
   ],
   "source": [
    "#2.8 #删除取值过于单一（比如某个值出现了99%以上）的特征\n",
    "overfit = []\n",
    "for i in X.columns:\n",
    "    counts = X[i].value_counts()\n",
    "    zeros = counts.iloc[0]\n",
    "    if zeros / len(X) * 100 > 99.94: #改进点7：99.94是可以调整的，80,90,95，99...\n",
    "        overfit.append(i)\n",
    "\n",
    "overfit = list(overfit)\n",
    "overfit.append('MSZoning_C (all)')\n",
    "\n",
    "X = np.array(X.drop(overfit, axis=1).copy())\n",
    "y = np.array(y)\n",
    "X_sub = np.array(X_sub.drop(overfit, axis=1).copy())\n",
    "\n",
    "print('X', X.shape, 'y', y.shape, 'X_sub', X_sub.shape)\n",
    "\n",
    "print('feature engineering finished!', datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfolds = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "\n",
    "#定义均方根对数误差（Root Mean Squared Logarithmic Error ，RMSLE）\n",
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mse(y, y_pred))\n",
    "\n",
    "#创建模型评分函数\n",
    "def cv_rmse(model, X=X):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=kfolds))\n",
    "    return (rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Advanced Regression Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.1 parameters(for grid search)\n",
    "alphas_alt = [14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5]\n",
    "alphas2 = [5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008]\n",
    "e_alphas = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007]\n",
    "e_l1ratio = [0.8, 0.85, 0.9, 0.95, 0.99, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### single model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.2 single model\n",
    "#改进点9:more models\n",
    "#改进点10: 对svr，GradientBoostingRegressor，LGBMRegressor，XGBRegressor等做GridSearchCV\n",
    "#ridge\n",
    "ridge = make_pipeline(RobustScaler(), RidgeCV(alphas=alphas_alt, cv=kfolds))\n",
    "\n",
    "#lasso\n",
    "lasso = make_pipeline(\n",
    "    RobustScaler(),\n",
    "    LassoCV(max_iter=1e7, alphas=alphas2, random_state=42, cv=kfolds))\n",
    "\n",
    "#elastic net\n",
    "elasticnet = make_pipeline(\n",
    "    RobustScaler(),\n",
    "    ElasticNetCV(max_iter=1e7, alphas=e_alphas, cv=kfolds, l1_ratio=e_l1ratio))\n",
    "\n",
    "#svm\n",
    "svr = make_pipeline(RobustScaler(), SVR(\n",
    "    C=20,\n",
    "    epsilon=0.008,\n",
    "    gamma=0.0003,\n",
    "))\n",
    "\n",
    "#GradientBoosting（展开到一阶导数）\n",
    "gbr = GradientBoostingRegressor(n_estimators=3000,\n",
    "                                learning_rate=0.05,\n",
    "                                max_depth=4,\n",
    "                                max_features='sqrt',\n",
    "                                min_samples_leaf=15,\n",
    "                                min_samples_split=10,\n",
    "                                loss='huber',\n",
    "                                random_state=42)\n",
    "\n",
    "#lightgbm\n",
    "lightgbm = LGBMRegressor(\n",
    "    objective='regression',\n",
    "    num_leaves=4,\n",
    "    learning_rate=0.01,\n",
    "    n_estimators=5000,\n",
    "    max_bin=200,\n",
    "    bagging_fraction=0.75,\n",
    "    bagging_freq=5,\n",
    "    bagging_seed=7,\n",
    "    feature_fraction=0.2,\n",
    "    feature_fraction_seed=7,\n",
    "    verbose=-1,\n",
    "    #min_data_in_leaf=2,\n",
    "    #min_sum_hessian_in_leaf=11\n",
    ")\n",
    "\n",
    "#xgboost（展开到二阶导数）\n",
    "xgboost = XGBRegressor(learning_rate=0.01,\n",
    "                       n_estimators=3460,\n",
    "                       max_depth=3,\n",
    "                       min_child_weight=0,\n",
    "                       gamma=0,\n",
    "                       subsample=0.7,\n",
    "                       colsample_bytree=0.7,\n",
    "                       objective='reg:linear',\n",
    "                       nthread=-1,\n",
    "                       scale_pos_weight=1,\n",
    "                       seed=27,\n",
    "                       reg_alpha=0.00006)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ensemble:\n",
    "* 先对单模型做stacking模型\n",
    "* 再对单模型+stacking做一次linear blending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.3 stacking\n",
    "#StackingCVRegressor：A 'Stacking Cross-Validation' regressor for scikit-learn estimators.\n",
    "#regressors=(...)中并没有纳入前面的svr模型,似乎纳入svr之后性能反而变差(why?)：stacking模型的性能0.11748--->0.11873\n",
    "stack_gen = StackingCVRegressor(regressors=(ridge, lasso, elasticnet, gbr, xgboost, lightgbm),\n",
    "                                meta_regressor=xgboost,\n",
    "                                use_features_in_secondary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST score on CV\n",
      "Ridge score: 0.1024 (0.0144)\n",
      " 2024-04-05 17:32:26.528112\n",
      "Lasso score: 0.1031 (0.0148)\n",
      " 2024-04-05 17:32:36.971687\n",
      "ElasticNet score: 0.1032 (0.0149)\n",
      " 2024-04-05 17:33:20.954334\n",
      "SVR score: 0.1024 (0.0133)\n",
      " 2024-04-05 17:33:26.052249\n",
      "Lightgbm score: 0.1066 (0.0153)\n",
      " 2024-04-05 17:33:53.618367\n",
      "GradientBoosting score: 0.1076 (0.0129)\n",
      " 2024-04-05 17:36:14.808131\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:30] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:35] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:36:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:37:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xgboost score: 0.1088 (0.0160)\n",
      " 2024-04-05 17:37:07.402298\n"
     ]
    }
   ],
   "source": [
    "#3.4 观察单模型的效果\n",
    "print('TEST score on CV')\n",
    "\n",
    "score = cv_rmse(ridge) #cross_val_score(RidgeCV(alphas),X, y) 外层k-fold交叉验证, 每次调用modelCV.fit时内部也会进行k-fold交叉验证\n",
    "print(\"Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), ) #0.1024\n",
    "\n",
    "score = cv_rmse(lasso)\n",
    "print(\"Lasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), ) #0.1031\n",
    "\n",
    "score = cv_rmse(elasticnet)\n",
    "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )#0.1031 \n",
    "\n",
    "score = cv_rmse(svr)\n",
    "print(\"SVR score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), ) #0.1023\n",
    "\n",
    "score = cv_rmse(lightgbm)\n",
    "print(\"Lightgbm score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )#0.1061\n",
    "\n",
    "score = cv_rmse(gbr)\n",
    "print(\"GradientBoosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )#0.1072\n",
    "\n",
    "score = cv_rmse(xgboost)\n",
    "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), ) #0.1064"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START Fit\n",
      "2024-04-05 17:47:47.626361 StackingCVRegressor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:49:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:49:34] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:49:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:49:47] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:49:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:50:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:50:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#3.5 train the stacking model\n",
    "#stacking 3步走(可不用管细节，fit会完成stacking的整个流程)：\n",
    "#1.1 learn first-level model\n",
    "#1.2 construct a training set for second-level model\n",
    "#2. train the second-level model:学习第2层的模型，也就是学习如何融合第1层的模型\n",
    "#3. re-learn first-level model on the entire train set\n",
    "print('START Fit')\n",
    "print(datetime.now(), 'StackingCVRegressor')\n",
    "stack_gen_model = stack_gen.fit(X, y) #Fit ensemble regressors and the meta-regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict submission 2024-04-05 17:51:42.132077\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>127475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>161385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>183990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>203330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>183953.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461   127475.0\n",
       "1  1462   161385.0\n",
       "2  1463   183990.0\n",
       "3  1464   203330.0\n",
       "4  1465   183953.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.1 submit stacking result\n",
    "print('Predict submission', datetime.now(),)\n",
    "submission = pd.read_csv(\"./data/sample_submission.csv\")\n",
    "submission.iloc[:,1] = np.floor(np.expm1(stack_gen_model.predict(X_sub)))\n",
    "submission.head()\n",
    "submission.to_csv(\"submission_stacking.csv\", index=False) #0.11674"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linear blending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-05 17:51:44.528591 ridge\n",
      "Pipeline(steps=[('robustscaler', RobustScaler()),\n",
      "                ('ridgecv',\n",
      "                 RidgeCV(alphas=array([14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3, 15.4, 15.5]),\n",
      "                         cv=KFold(n_splits=10, random_state=42, shuffle=True)))])\n",
      "2024-04-05 17:51:46.035157 lasso\n",
      "2024-04-05 17:51:47.085525 elasticnet\n",
      "2024-04-05 17:51:51.485588 GradientBoosting\n",
      "2024-04-05 17:52:05.024061 xgboost\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frank\\AppData\\Roaming\\Python\\Python38\\site-packages\\xgboost\\core.py:160: UserWarning: [17:52:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\objective\\regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-05 17:52:09.831592 lightgbm\n",
      "2024-04-05 17:52:12.206129 svr\n"
     ]
    }
   ],
   "source": [
    "#4.1 在整个训练集上重新训练第1层的单模型和svr，后面blending用(如果直接拿stacking第1层的模型，会报not fit的错误)\n",
    "print(datetime.now(), 'ridge')\n",
    "ridge_model_full_data = ridge.fit(X, y)\n",
    "print(ridge_model_full_data)\n",
    "print(datetime.now(), 'lasso')\n",
    "lasso_model_full_data = lasso.fit(X, y)\n",
    "print(datetime.now(), 'elasticnet')\n",
    "elastic_model_full_data = elasticnet.fit(X, y)\n",
    "print(datetime.now(), 'GradientBoosting')\n",
    "gbr_model_full_data = gbr.fit(X, y)\n",
    "print(datetime.now(), 'xgboost')\n",
    "xgb_model_full_data = xgboost.fit(X, y)\n",
    "print(datetime.now(), 'lightgbm')\n",
    "lgb_model_full_data = lightgbm.fit(X, y)\n",
    "print(datetime.now(), 'svr')\n",
    "svr_model_full_data = svr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#待混合的models\n",
    "models = [\n",
    "    ridge_model_full_data, lasso_model_full_data, elastic_model_full_data,\n",
    "    gbr_model_full_data, xgb_model_full_data, lgb_model_full_data,\n",
    "    svr_model_full_data, stack_gen_model\n",
    "]\n",
    "len(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear blending coefficients: public coefs\n",
    "#order: ridge, lasso, elasticnet, gbr, xgboost, lightgbm, svr, stack\n",
    "public_coefs = [0.1, 0.1, 0.1, 0.1, 0.15, 0.1, 0.1, 0.25]\n",
    "bias = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_blend_models_predict(data_x,models,coefs, bias):\n",
    "    tmp=[model.predict(data_x) for model in models]\n",
    "    tmp = [c*d for c,d in zip(coefs,tmp)]\n",
    "    pres=np.array(tmp).swapaxes(0,1) #numpy中的reshape不能用于交换维度，一开始的种种问题，皆由此来\n",
    "    pres=np.sum(pres,axis=1)\n",
    "    return pres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blending models RMSLE score on train data:\n",
      "0.05909249946569733\n",
      "Predict submission 2024-04-05 18:11:21.990720\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>123033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>158998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>185878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>200048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>188495.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461   123033.0\n",
       "1  1462   158998.0\n",
       "2  1463   185878.0\n",
       "3  1464   200048.0\n",
       "4  1465   188495.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.2 submit blend_models_with_public_coefs \n",
    "\n",
    "print('blending models RMSLE score on train data:')\n",
    "print(rmsle(y, linear_blend_models_predict(X,models,public_coefs, bias)))\n",
    "\n",
    "#before Blend with Top Kernals submissions\n",
    "print('Predict submission', datetime.now(),)\n",
    "submission = pd.read_csv(\"./data/sample_submission.csv\")\n",
    "submission.iloc[:,1] = np.floor(np.expm1(linear_blend_models_predict(X_sub,models,public_coefs, bias))) #expm1: exp(x) - 1; 注意还要取整\n",
    "# submission.iloc[:,1] = np.expm1(blend_models_predict(X_sub)) \n",
    "submission.head()\n",
    "submission.to_csv(\"submission_blend_models_with_public_coefs.csv\", index=False) #0.11413"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear blending coefficients: coefs got by linear regression \n",
    "#注意：这里很容易过拟合，所以alphas3的不宜过小\n",
    "#alphas3 = np.linspace(0,1e3,1001) #如果从０开始，RidgeCV会选择０，train_rmse:0.027818，但test_rmse会很大，即过拟合！\n",
    "#改进点11：如何得到更合适的系数\n",
    "alphas3 = [70] #可以继续优化，使train_rmse与public_coefs的结果接近\n",
    "\n",
    "def blend_models(train_x, train_y, models):\n",
    "    tmp = [model.predict(train_x) for model in models]\n",
    "    pres = np.array(tmp).swapaxes(0,1) #一开始用的reshape，注意这与pytorch中不同，不能用于多维的维度间的交换！！！\n",
    "    print(pres.shape)  #(1457,8)\n",
    "    #注意要设置fit_intercept=False，否则bias会很大，占主导地位，而系数coef_都很小\n",
    "    #fit_intercept=False时不求截距，但要求数据提前中心化，并且此时会忽略normalize参数\n",
    "    #linear = LinearRegression(fit_intercept=False)\n",
    "    linear = RidgeCV(alphas=alphas3,\n",
    "                     cv=kfolds,\n",
    "                     fit_intercept=False,\n",
    "                     scoring=make_scorer(rmsle, greater_is_better=False)\n",
    "                    )\n",
    "    linear = linear.fit(pres, train_y)\n",
    "    print('linear coefficient:')\n",
    "    print(linear.coef_)\n",
    "    print('linear bias:')\n",
    "    print(linear.intercept_)\n",
    "    print('best alpha: %f'%(linear.alpha_))\n",
    "    print('best score: %f'%(rmsle(linear.predict(pres), train_y)))\n",
    "    return linear.coef_, linear.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1453, 8)\n",
      "linear coefficient:\n",
      "[0.09885513 0.09334875 0.09315617 0.16456569 0.1539322  0.12433016\n",
      " 0.11124578 0.16045629]\n",
      "linear bias:\n",
      "0.0\n",
      "best alpha: 70.000000\n",
      "best score: 0.059200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9998901838894283"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可以对coefs归一化\n",
    "coefs, bias = blend_models(X, y, models)\n",
    "sum(coefs)\n",
    "# coefs=[i/sum(coefs) for i in coefs.tolist()]\n",
    "# coefs\n",
    "# from scipy.special import softmax\n",
    "# coefs=softmax(coefs)\n",
    "# coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blending models RMSLE score on train data:\n",
      "0.059200490178674084\n",
      "Predict submission 2024-04-05 18:11:41.594832\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>122775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>158820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>186046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>199096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>188442.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461   122775.0\n",
       "1  1462   158820.0\n",
       "2  1463   186046.0\n",
       "3  1464   199096.0\n",
       "4  1465   188442.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.3 submit blend_models_with_regression_coefs \n",
    "print('blending models RMSLE score on train data:')\n",
    "print(rmsle(y, linear_blend_models_predict(X,models,coefs,bias))) #0.059305\n",
    "\n",
    "#before Blend with Top Kernals submissions\n",
    "print('Predict submission', datetime.now(),)\n",
    "submission = pd.read_csv(\"./data/sample_submission.csv\")\n",
    "submission.iloc[:,1] = np.floor(np.expm1(linear_blend_models_predict(X_sub,models,coefs,bias))) #expm1: exp(x) - 1; 注意还要取整\n",
    "# submission.iloc[:,1] = np.expm1(blend_models_predict(X_sub)) #expm1: exp(x) - 1; 注意还要取整\n",
    "submission.head()\n",
    "submission.to_csv(\"submission_blend_models_with_regression_coefs.csv\", index=False) #0.11492 可以得到相当的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Blend with Top Kernals submissions 2024-04-05 18:08:03.636221\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../input/top-10-0-10943-stacking-mice-and-brutal-force/House_Prices_submit.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-67-412ded11d015>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Blend with Top Kernals submissions'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msub_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/top-10-0-10943-stacking-mice-and-brutal-force/House_Prices_submit.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0msub_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/hybrid-svm-benchmark-approach-0-11180-lb-top-2/hybrid_solution.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0msub_3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/lasso-model-for-regression-problem/lasso_sol.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m         \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/top-10-0-10943-stacking-mice-and-brutal-force/House_Prices_submit.csv'"
     ]
    }
   ],
   "source": [
    "#4.4 mixing with the top kernels\n",
    "\n",
    "print('Blend with Top Kernals submissions', datetime.now(),)\n",
    "sub_1 = pd.read_csv('submission_base_blend_top_Scale extremes2.csv')\n",
    "sub_2 = pd.read_csv('submission_blend_models_with_public_coefs.csv')\n",
    "sub_3 = pd.read_csv('submission_blend_models_with_regression_coefs.csv')\n",
    "submission.iloc[:,1] = np.floor((0.25 * np.floor(np.expm1(linear_blend_models_predict(X_sub,models,public_coefs, bias)))) + \n",
    "                                (0.25 * sub_1.iloc[:,1]) + \n",
    "                                (0.25 * sub_2.iloc[:,1]) + \n",
    "                                (0.25 * sub_3.iloc[:,1]))  \n",
    "submission.to_csv(\"submission_blend_top.csv\", index=False) #0.11115\n",
    "print('Save submission', datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save submission 2024-04-05 18:06:25.123521\n"
     ]
    }
   ],
   "source": [
    "#4.5 Brutal approach to deal with predictions close to outer range \n",
    "#第超低的房价更低，让超高的房价更高(通常来说，会将小者放大，大者缩小，但房价有其特殊性：有些偏远地区的房子比预测更低，\n",
    "#有些房子比预测高得多)，这里让这两种极端情况更极端一些，这样更符合房价的特性\n",
    "#注意缩放的分位数0.005,0.995以及缩放系数0.77,1.1可以适当调整，相关public kernel中并未提到这块儿的参数如何选择，猜测：唯结果论\n",
    "q1 = submission['SalePrice'].quantile(0.0045) \n",
    "q2 = submission['SalePrice'].quantile(0.998)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*0.77)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*1.1)\n",
    "\n",
    "submission.to_csv(\"submission_blend_top_Scale extremes.csv\", index=False) #0.10647(best result)\n",
    "print('Save submission', datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../input/house-price-best/submission_best.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-82940e030b78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msubmission\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/house-price-best/submission_best.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msubmission\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"submission_best.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#0.10647(best result)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Save submission best'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m         \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/house-price-best/submission_best.csv'"
     ]
    }
   ],
   "source": [
    "submission = pd.read_csv('../input/house-price-best/submission_best.csv')\n",
    "submission.to_csv(\"submission_best.csv\", index=False) #0.10647(best result)\n",
    "print('Save submission best', datetime.now())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.Q & A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 有必要重新训练一遍第一层的模型吗？在训练stacking时的3步中应该是包含了这一步的\n",
    "由下面的代码可见，**训练完stacking之后，如果要与各个单模型做blending,确实有必要再重新训练第1层的模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.2291767 , 12.19272327, 12.27214818, ..., 12.46645445,\n",
       "       11.85934606, 11.9406427 ])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#如果不重新训练每个单模型(跳过4.1)，直接拿stack_gen_model.regressors来用，会发现：\n",
    "#只有第一个模型能直接predict，其他模型会报not fit错误\n",
    "stack_gen_model.regressors[0].predict(X)\n",
    "#stack_gen_model.regressors[1].predict(X) #error\n",
    "#stack_gen_model.regressors[2].predict(X) #error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 detect outliers\n",
    "* outliers一般可以通过散点图观察而得\n",
    "* 可以通过LocalOutlierFactor自动检测，详见前面的detect_outliers函数\n",
    "\n",
    "但很多public kernels中的outliers=outliers = [30, 88, 462, 523, 632, 1298, 1324],有些很容易找到，剩下的完全不知道哪儿来的！！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#how to optimize hyperparameters?\n",
    "# from pactools.grid_search import GridSearchCVProgressBar #if u need progress bar\n",
    "# def grid_search(model, parameters, train_x, train_y, progress_bar=False, cv=5):\n",
    "#     #sklearn的0.22版本默认采用5-fold cv，当前版本默认3折\n",
    "#     models = GridSearchCVProgressBar(\n",
    "#         model, parameters, cv=cv, verbose=1,\n",
    "#         n_jobs=6) if progress_bar else GridSearchCV(\n",
    "#             model, parameters, cv=cv, n_jobs=6)\n",
    "#     models.fit(train_x, train_y)\n",
    "#     print(models.best_params_)\n",
    "#     print(models.best_score_)\n",
    "#     #print(models.best_estimator_)\n",
    "\n",
    "# params1 = {\n",
    "#     'alpha':\n",
    "#     [0.1, 0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n",
    "# }\n",
    "# grid_search(Ridge(), params1, X, y, progress_bar=True, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Why scale extreme values?\n",
    "* 一般情况下：小者放大，大者缩小，让极端值尽可能变得正常\n",
    "* 但房价刚好与之相反：**让极端值更极端！**因为这才是房价！比如有些偏远地区的房子会比预测更低，有些房子比预测高得多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save submission 2024-04-05 18:09:45.004750\n"
     ]
    }
   ],
   "source": [
    "#一般情况下：小者放大，大者缩小，让极端值尽可能变得正常,但不适用于本问题\n",
    "q1 = submission['SalePrice'].quantile(0.005)\n",
    "q2 = submission['SalePrice'].quantile(0.995)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*1.1)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*0.77)\n",
    "\n",
    "submission.to_csv(\"submission_base_blend_top_Scale extremes2.csv\", index=False) #0.11602 反效果\n",
    "print('Save submission', datetime.now())"
   ]
  }
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